The National Science Foundation awarded a $350,000 Project Grant to the University of Washington under the Mathematical and Physical Sciences federal grant program (CFDA 47.049) to develop novel strategies for constructing optimal statistical estimators using machine learning tools. Over a three-year period ending August 2025, the investigators will study representations of the efficient influence function that can be computed numerically to derive new asymptotically efficient estimators. They...
This Project Grant award of $250,000 from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports research led by Carnegie Mellon University to develop flexible and valid inference procedures for modern complex data that leverage powerful black-box machine learning algorithms. The key focus is on advancing cross-validation techniques to enable adaptive inference in conjunction with these opaque models, with potential applications in areas such as...
This $149,989 Project Grant award from the National Science Foundation (NSF) Mathematical and Physical Sciences (CFDA 47.049) program will support research to develop statistical models and inference methods for analyzing random point processes. The research will provide tools for analyzing time series of point process data, with applications in fields such as national security, economics, neuroscience, and geosciences. Key activities include developing parameter estimation procedures,...
This $400,000 National Science Foundation Project Grant, awarded under the Mathematical and Physical Sciences program (CFDA 47.049), will support the development of statistical methods and machine learning techniques for analyzing complex structured and count data. Over a three-year period ending in August 2025, the University of Washington will advance the state of knowledge in big structured and count data analysis through two tracks of research. The first track will focus on revising and...
This Project Grant award of $146,738 from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports collaborative statistical research on multivariate and functional time series analysis. The research will develop new nonparametric inference procedures that can accommodate a wide range of data dimensionality and require weak assumptions on the data generating processes. The methodology will be disseminated through publications, presentations, and...
This $120,000 federal Project Grant award from the National Science Foundation's Mathematical and Physical Sciences (CFDA 47.049) program supports research at the University of Washington to develop mathematical frameworks, algorithms, and computational methodologies for scientific machine learning using Gaussian processes. The key focus areas are: (1) using Gaussian processes to solve nonlinear, high-dimensional, and parametric partial differential equations; (2) Gaussian process-based...
This $175,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) supports the development of innovative statistical methods known as "hunt-and-test procedures." These methods aim to reliably detect meaningful signals in complex data while avoiding false discoveries due to "double dipping" - the unintentional use of the same data for both identifying and testing hypotheses. The project has two primary...
This $169,999 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports collaborative research at the University of California, Davis (UC Davis) to advance innovative nonparametric data analysis techniques. The project aims to conduct comprehensive statistical and computational analyses to push the boundaries of modern nonparametric statistical inference, with potential applications in areas like nonparametric latent...
This Project Grant award of $179,999 from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports comprehensive statistical and computational analyses with the goal of advancing innovative nonparametric data analysis techniques. The research aims to push the boundaries of modern nonparametric statistical inference and develop methodologies applicable to areas such as latent variable models, time series analysis, and sequential nonparametric...
This $125,000 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports collaborative research on advancing the theory and practice of causal learning using machine learning methods. The project aims to develop new approaches for imputing unobserved counterfactual outcomes, quantifying uncertainty in treatment effect estimation, and establishing a statistical framework for finite-population inference. The work will...